Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity an...Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity and compression ratio joint adjustment algorithm for compressed spectrum sensing in CR network is investigated, with the hypothesis that the sparsity level is unknown as priori knowledge at CR terminals. As perfect spectrum reconstruction is not necessarily required during spectrum detection process, the proposed algorithm only performs a rough estimate of sparsity level. Meanwhile, in order to further reduce the sensing measurement, different compression ratios for CR terminals with varying Signal-to-Noise Ratio (SNR) are considered. The proposed algorithm, which optimizes the compression ratio as well as the estimated sparsity level, can greatly reduce the sensing measurement without degrading the detection performance. It also requires less steps of iteration for convergence. Corroborating simulation results are presented to testify the effectiveness of the proposed algorithm for collaborative spectrum sensing.展开更多
Accurate determination of seismic velocity of the crust is important for understanding regional tectonics and crustal evolution of the Earth. We propose a stepwise joint linearized inversion method using surface wave ...Accurate determination of seismic velocity of the crust is important for understanding regional tectonics and crustal evolution of the Earth. We propose a stepwise joint linearized inversion method using surface wave dispersion, Rayleigh wave ZH ratio （i.e., ellipticity）, and receiver function data to better resolve 1D crustal shear wave velocity （Vs） structure. Surface wave dispersion and Rayleigh wave ZH ratio data are more sensitive to absolute variations of shear wave speed at depths, but their sensi- tivity kernels to shear wave speeds are different and complimentary. However, receiver function data are more sensitive to sharp velocity contrast （e.g., due to the existence of crustal interfaces） and Vp/Vs ratios. The stepwise inversion method takes advantages of the complementary sensitivities of each dataset to better constrain the Vs model in the crust. We firstly invert surface wave dispersion and ZH ratio data to obtain a 1D smooth absolute vs model and then incorporate receiver function data in the joint inver- sion to obtain a finer Vs model with better constraints on interface structures. Through synthetic tests, Monte Carlo error analyses, and application to real data, we demonstrate that the proposed joint inversion method can resolve robust crustal Vs structures and with little initial model dependency.展开更多
基金Supported by the National Natural Science Foundation of China (No. 61102066)China Postdoctoral Science Foundation (No. 2012M511365)the Scientific Research Project of Zhejiang Provincial Education Department (No.Y201119890)
文摘Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity and compression ratio joint adjustment algorithm for compressed spectrum sensing in CR network is investigated, with the hypothesis that the sparsity level is unknown as priori knowledge at CR terminals. As perfect spectrum reconstruction is not necessarily required during spectrum detection process, the proposed algorithm only performs a rough estimate of sparsity level. Meanwhile, in order to further reduce the sensing measurement, different compression ratios for CR terminals with varying Signal-to-Noise Ratio (SNR) are considered. The proposed algorithm, which optimizes the compression ratio as well as the estimated sparsity level, can greatly reduce the sensing measurement without degrading the detection performance. It also requires less steps of iteration for convergence. Corroborating simulation results are presented to testify the effectiveness of the proposed algorithm for collaborative spectrum sensing.
基金supported by the National Earthquake Science Experiment in Sichuan and Yunnan Provinces of China(#2016 CESE 0201)National Natural Science Foundation of China(#41574034)China National Special Fund for Earthquake Scientific Research in Public Interest(#201508008)
文摘Accurate determination of seismic velocity of the crust is important for understanding regional tectonics and crustal evolution of the Earth. We propose a stepwise joint linearized inversion method using surface wave dispersion, Rayleigh wave ZH ratio （i.e., ellipticity）, and receiver function data to better resolve 1D crustal shear wave velocity （Vs） structure. Surface wave dispersion and Rayleigh wave ZH ratio data are more sensitive to absolute variations of shear wave speed at depths, but their sensi- tivity kernels to shear wave speeds are different and complimentary. However, receiver function data are more sensitive to sharp velocity contrast （e.g., due to the existence of crustal interfaces） and Vp/Vs ratios. The stepwise inversion method takes advantages of the complementary sensitivities of each dataset to better constrain the Vs model in the crust. We firstly invert surface wave dispersion and ZH ratio data to obtain a 1D smooth absolute vs model and then incorporate receiver function data in the joint inver- sion to obtain a finer Vs model with better constraints on interface structures. Through synthetic tests, Monte Carlo error analyses, and application to real data, we demonstrate that the proposed joint inversion method can resolve robust crustal Vs structures and with little initial model dependency.
